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What is Data Scientist?

Data Scientists, as the title suggests, deal with both structured and unstructured data from different sources. They are responsible for extracting useful information or knowledge from the extracted data. They usually have a variety of skills like mathematical, statistical, computing and trend-spotting. It is often dubbed as the most rewarding job in the current years.
In data science, the career path of a professional usually starts as Data Analyst, Business Intelligence Analyst, Software Engineer, or Test Analyst and with experience and skills or with the help of a certification course can become Data Scientist, Lead Data Scientist, Head of Product Analyst, and Director of Engineering.
Naukri Learning provides candidates with a variety of online course choices that would help them to become a professional data scientist. There are also a number of other related courses like Big Data Analyst, Hadoop, R, Python, Qlikview, Data Visualisation and Tableau.

Data Science Certification

A certification is like a step-up in your career and will establish your journey as an experienced data science professional.
Based on a Naukri survey, 67% of the recruiters mentioned that they prefer certified candidates and are also willing to pay higher.
Outlined below are the reasons to help you understand the advantages of a data science certification:
• Improve your current profile and resume with a certification
• With recognition, comes better salary and better job opportunities
• Your skills will be globally accepted and stand out as a certified professional
• Learn the advanced analytical techniques and skills
• Get to be proficient in the latest data analysis tools

Why Data Science

Data Science Career Path

After the completion of a data science training course, candidates can find opportunities in one of the professional fields immediately or in the future:
• Data Scientists
• Lead Data Scientist
• Product Analyst Manager
• Director of Engineering

Frequently Asked Questions

1. Why should I choose R programming for a data science project?

R language is useful for statistical computations, big data analysis and also for representing data graphically. In the past few years R has gained tremendous application in Big Data. Today 40% data scientists prefer R and remaining 34% prefer SAS and only about 26% go with Python. For a data science project, if you are confused between R and Python, please note that both the languages have their own pros and cons. But remember that R programming comes with advantages like Data wrangling (a way to clean messy and complex data sets so as to enable convenient consumption of data for further analysis) and all the R libraries focus on making one thing certain - to make data analysis easier. In the end it's up to you to choose it..

All major statistical software packages (like SAS, R, SPSS, Stata etc.) are similar in functionality. Some aspects may differ or may be better than another (for instance, graphics), but on the whole, most of these software packages were designed to manage and analyze data. SPSS has a powerful proprietary command syntax language. But it cannot be used to write programming codes for complex data cleaning and analyses all the time. SAS and Stata analysts are similar in functionality to SPSS as they can be used alternatively. Lastly, R is popular as it is open source as well as sophisticated..

3. How do you optimize a web crawler to run much faster, extract better information and summarize data to produce cleaner databases?

Putting a time-out threshold to less than 2 seconds, extracting no more than the first 20 KB of each pages, and not revisiting pages already crawled (that is, avoid recurrence in your algorithm) are good starting points.

4. How is machine learning different from data science?

Machine learning as well as statistical principles are a small part of data science. Algorithms applied in machine learning are data dependent and apply a training set so as to fine-tune a model for algorithmic parameters. Most of them comprise of techniques like regression, naive Bayes or supervised clustering..

5. You are about to send a million emails under a marketing campaign. How will you optimize delivery and its response using data science principles?

That is not really possible. However, a lot of data is available to marketers through website analytics, especially via Email service providers and ecommerce platforms. Adequate information can be gathered pertaining to user/consumer behavior by virtue of data science. This ranges through their preferences, choices, most preferred time, and favorite engagement medium. Data science also helps marketers to make future business predictions basing the past actions. Hence, data science is a latent tool that helps to market the most relevant products and services to a target user. Plus, it is useful to shut down abusers and spammers with the application of sophisticated AI models. This enables a spam free inbox for the user..

6. How would you turn unstructured data into structured data in data science projects?

NLP and Information Extraction are the processes to do it. Suppose you are having a template that needs to be filled with data extracted from an unstructured information feed. Honestly, this is a very basic method of creating structured data out of an unstructured feed. Based on research, you can also discover structures of data from unstructured data. While there will be no template in the same you can construct a graph with multiple nodes which in a way represent data extracts as well as links that represent how or why information that is related to each other gets fragmented..

7. What is the difference between Big Data and Data Science?

The term ‘Big Data’ is popularly used to describe exponential growth and availability of data. This data can be present in both structured and unstructured formats. Anybody who works on this data or deals with it in some way or the other to process, analyze or make sense of such massive amounts of data is a Big Data Professional. Whereas, Data Scientists are basically given the task of investigating complex problems. They apply their knowledge of mathematics and statistical principles in conjunction with computer science algorithms to arrive at answers. Such areas not only represent their knowledge but also portray that they are the most proficient Scientists of data..

8. Is it worth learning about data science?

Data Science is an ever-growing industry with a lot of scope so yes, it's worth learning. Data Scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

9. What are the best resources to learn how to use Python for Machine Learning and Data Science?

You can learn Python through tutorials that cover concepts from beginner to advanced levels. Multiple eLearning portals provide such tutorials. You can learn Python from Books, tutorials, from MOOCs, from Paid classroom courses, from YouTube and also from live Applications. However, you cannot become a good data scientist by just learning Python. You should master Data Science with Python which covers programming with Python, Database Technologies, a good hold on Mathematics and Statistical principles as well as Machine Learning with Python. You should also be good with Information Retrieval..

10. Define data mining and data science and how are they different?

Data mining is a process used by data scientists and machine learning engineers. Data mining categorizes a family of algorithms and it is all about the process to discover data patterns. Data scientists create data products from data centric applications. It is a technical ability to handle data and scientific methods to assess its impact on a project, product or organization. Both are needed to build data products with machine learning algorithms. Such data products don’t empower business users but systems as a data scientist applies data mining processes to learn algorithms are used..

here are numerous tools associated with data science. If you are someone who is new to the field, you will be overwhelmed and intimidated by the number of data science tools. This article is for those who are looking to move to a data science field or have just started and want to know which tools can help them start their career as a data scientist

Data Science is an inter-disciplinary field which is associated with the extraction of information and insights from data through scientific methods, processes, and systems. It can be in various forms, either structured or unstructured. With the high use of analytics by businesses to gain the competitive edge in the industry, the demand for good data science professionals have gone high in the job market

Data science has become one of the most attractive profiles in the recent years, with Harvard Business Review giving it the title of “Sexiest Job of the 21st Century.” As there is a high demand for experienced people in data science, professionals from other backgrounds have shown an interest in pursuing a career in this field.

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Artificial intelligence (AI) is a field of computer science that enables computers and machines to perform tasks normally requiring human intelligence. Its many applications range from chess-playing robots and autonomous cars to speech, image, and language processing, robotic manufacturing, and surveillance systems. In the twenty-first century, AI has experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding. AI techniques have now become an essential part of the technology industry, helping to solve many challenging problems in computer science

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